{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67f102dd",
   "metadata": {},
   "source": [
    "## 案例背景"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a8dbcf",
   "metadata": {},
   "source": [
    "公司是一家大型礼品公司，主要经营在线零售业务，其客户来源于世界各地。近年来，随着电商行业的发展，竞争也\n",
    "变得越来\n",
    "越激烈。 公司为了能够在竞争中取得优势，争取主动，就必须要维护好现有的客户，做好客户运营，“酒香不怕巷\n",
    "子深”的时代，已经一去不返了。\n",
    "客户者，国之大事，死生之地，存亡之道，不可不察也。\n",
    "不过，再怎样维护客户关系，也不可能对所有的客户都等同视之。对 公司来说，其客户种类众多，行为不一，对 \n",
    "公司的贡献程度亦是大相径庭。因此，传统的手工维护方式会存在很大的困难， 公司迫切需要一种更精准高效的方式，来挖掘\n",
    "客户的价值，并根据价值对客户分门别类进行管理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d641a02",
   "metadata": {},
   "source": [
    "公司收集了2010年12月1日至2011年12月9日所有的客户购买记录，假设当前的时间为2012年1月1日，我们的任务，\n",
    "就是要从该数据集中，挖掘出所有客户的价值，并根据客户的价值来进行分组管理，采取不同的策略\n",
    "注意：在InvoiceNo订单编号中，如果以C开头表示订单被取消。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfddf268",
   "metadata": {},
   "source": [
    "## 任务与实现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1c75a2",
   "metadata": {},
   "source": [
    "### 导入相关的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f519e96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "sns.set(style=\"darkgrid\", font_scale=1.2)\n",
    "\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef027de",
   "metadata": {},
   "source": [
    "### 数据的读取与处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bb404def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010/12/1 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010/12/1 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010/12/1 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010/12/1 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010/12/1 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011/12/9 12:50</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011/12/9 12:50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011/12/9 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011/12/9 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011/12/9 12:50</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1         536365     71053                  WHITE METAL LANTERN         6   \n",
       "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "...          ...       ...                                  ...       ...   \n",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0        2010/12/1 8:26       2.55     17850.0  United Kingdom  \n",
       "1        2010/12/1 8:26       3.39     17850.0  United Kingdom  \n",
       "2        2010/12/1 8:26       2.75     17850.0  United Kingdom  \n",
       "3        2010/12/1 8:26       3.39     17850.0  United Kingdom  \n",
       "4        2010/12/1 8:26       3.39     17850.0  United Kingdom  \n",
       "...                 ...        ...         ...             ...  \n",
       "541904  2011/12/9 12:50       0.85     12680.0          France  \n",
       "541905  2011/12/9 12:50       2.10     12680.0          France  \n",
       "541906  2011/12/9 12:50       4.15     12680.0          France  \n",
       "541907  2011/12/9 12:50       4.15     12680.0          France  \n",
       "541908  2011/12/9 12:50       4.95     12680.0          France  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('data.csv')\n",
    "data\n",
    "# InvoiceNo为订单编号，以'C'开头表示，订单已取消"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8149e8",
   "metadata": {},
   "source": [
    "由上面的数据可知，总计54万多条数据，共计8个字段。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd698351",
   "metadata": {},
   "source": [
    "#### 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "357b9a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# 使用info()方法查看数据数量\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e335a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将Description的空值填充为‘无’\n",
    "data['Description'].fillna('无',inplace=True)\n",
    "# 将消费id的空值删除掉。\n",
    "data.dropna(subset=['CustomerID'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7ab654a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    406829 non-null  object \n",
      " 1   StockCode    406829 non-null  object \n",
      " 2   Description  406829 non-null  object \n",
      " 3   Quantity     406829 non-null  int64  \n",
      " 4   InvoiceDate  406829 non-null  object \n",
      " 5   UnitPrice    406829 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      406829 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d1371226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看空值的数量\n",
    "data.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cb47a9",
   "metadata": {},
   "source": [
    "#### 异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "428dfa50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>406829</td>\n",
       "      <td>406829</td>\n",
       "      <td>406829</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>22190</td>\n",
       "      <td>3684</td>\n",
       "      <td>3896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20460</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>576339</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011/11/14 15:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>542</td>\n",
       "      <td>2077</td>\n",
       "      <td>2070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.061303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.460471</td>\n",
       "      <td>15287.690570</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>248.693370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.315162</td>\n",
       "      <td>1713.600303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15152.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16791.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80995.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description       Quantity  \\\n",
       "count     406829    406829                              406829  406829.000000   \n",
       "unique     22190      3684                                3896            NaN   \n",
       "top       576339    85123A  WHITE HANGING HEART T-LIGHT HOLDER            NaN   \n",
       "freq         542      2077                                2070            NaN   \n",
       "mean         NaN       NaN                                 NaN      12.061303   \n",
       "std          NaN       NaN                                 NaN     248.693370   \n",
       "min          NaN       NaN                                 NaN  -80995.000000   \n",
       "25%          NaN       NaN                                 NaN       2.000000   \n",
       "50%          NaN       NaN                                 NaN       5.000000   \n",
       "75%          NaN       NaN                                 NaN      12.000000   \n",
       "max          NaN       NaN                                 NaN   80995.000000   \n",
       "\n",
       "             InvoiceDate      UnitPrice     CustomerID         Country  \n",
       "count             406829  406829.000000  406829.000000          406829  \n",
       "unique             20460            NaN            NaN              37  \n",
       "top     2011/11/14 15:27            NaN            NaN  United Kingdom  \n",
       "freq                 543            NaN            NaN          361878  \n",
       "mean                 NaN       3.460471   15287.690570             NaN  \n",
       "std                  NaN      69.315162    1713.600303             NaN  \n",
       "min                  NaN       0.000000   12346.000000             NaN  \n",
       "25%                  NaN       1.250000   13953.000000             NaN  \n",
       "50%                  NaN       1.950000   15152.000000             NaN  \n",
       "75%                  NaN       3.750000   16791.000000             NaN  \n",
       "max                  NaN   38970.000000   18287.000000             NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    data.describe(include ='all' )\n",
    "#     猜测Quantity和UnitPrice的分布状态，Quantity对称分布，UnitPrcie呈右偏分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89387f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='UnitPrice', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 验证数据分布，绘制数据\n",
    "sns.set()\n",
    "fig,ax=plt.subplots(1,2)\n",
    "# 设置图形尺寸\n",
    "fig.set_size_inches(15,5)\n",
    "sns.distplot(data['Quantity'],hist=False,ax=ax[0])\n",
    "sns.distplot(data['UnitPrice'],hist=False,ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bfa63262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='UnitPrice'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 交叉验证,绘制箱线图\n",
    "fig,ax=plt.subplots(1,2)\n",
    "fig.set_size_inches(15,5)\n",
    "sns.boxplot(y='Quantity',data=data,ax=ax[0])\n",
    "sns.boxplot(y='UnitPrice',data=data,ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "380a62f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从以上两图交叉验证，可以得出，Quantity呈现对称分布，UnitPrice呈现右偏分布,上四分位数与最大值相差很多，则呈现右偏的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b5c3d3",
   "metadata": {},
   "source": [
    "#### 重复值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aca206e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5268"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4ba0635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>536409</td>\n",
       "      <td>21866</td>\n",
       "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/12/1 11:45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>536409</td>\n",
       "      <td>21866</td>\n",
       "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/12/1 11:45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>536409</td>\n",
       "      <td>22111</td>\n",
       "      <td>SCOTTIE DOG HOT WATER BOTTLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/12/1 11:45</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>536409</td>\n",
       "      <td>22111</td>\n",
       "      <td>SCOTTIE DOG HOT WATER BOTTLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/12/1 11:45</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>536409</td>\n",
       "      <td>22866</td>\n",
       "      <td>HAND WARMER SCOTTY DOG DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/12/1 11:45</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceNo StockCode                    Description  Quantity  \\\n",
       "494    536409     21866    UNION JACK FLAG LUGGAGE TAG         1   \n",
       "517    536409     21866    UNION JACK FLAG LUGGAGE TAG         1   \n",
       "485    536409     22111   SCOTTIE DOG HOT WATER BOTTLE         1   \n",
       "539    536409     22111   SCOTTIE DOG HOT WATER BOTTLE         1   \n",
       "489    536409     22866  HAND WARMER SCOTTY DOG DESIGN         1   \n",
       "\n",
       "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "494  2010/12/1 11:45       1.25     17908.0  United Kingdom  \n",
       "517  2010/12/1 11:45       1.25     17908.0  United Kingdom  \n",
       "485  2010/12/1 11:45       4.95     17908.0  United Kingdom  \n",
       "539  2010/12/1 11:45       4.95     17908.0  United Kingdom  \n",
       "489  2010/12/1 11:45       2.10     17908.0  United Kingdom  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep=False所有相同的都被标记为重复  此处重复值不可删除，一个订单可以含有多个商品，此处缺乏子类别。\n",
    "data[data.duplicated(keep=False)].sort_values(by=['InvoiceNo','StockCode']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e4b5e18",
   "metadata": {},
   "source": [
    "### RFM模型的构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bcbbd945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算消费金额 消费金额=数量*价格\n",
    "data['monetory']=data['Quantity']*data['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2f2e6b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 类型转化\n",
    "data['InvoiceDate']=pd.to_datetime(data['InvoiceDate'])\n",
    "data['CustomerID']=data['CustomerID'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df50071",
   "metadata": {},
   "source": [
    "#### 构建R值  \n",
    "\n",
    "r:用户最近的一次消费距离时间的间隔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba7cb5e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Recency</th>\n",
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       "    <tr>\n",
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       "      <td>24</td>\n",
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       "      <th>12348</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>202</td>\n",
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       "    <tr>\n",
       "      <th>18282</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency\n",
       "CustomerID         \n",
       "12346           347\n",
       "12347            24\n",
       "12348            97\n",
       "12349            40\n",
       "12350           332\n",
       "...             ...\n",
       "18280           299\n",
       "18281           202\n",
       "18282            29\n",
       "18283            25\n",
       "18287            64\n",
       "\n",
       "[4372 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_R(group_data,current):\n",
    "#     判断订单是否为退货订单，是的话则去掉\n",
    "    t=group_data[~group_data['InvoiceNo'].str.startswith('C')]\n",
    "    if len(t)==0:\n",
    "        delta=current-data['InvoiceDate'].min()\n",
    "    else:\n",
    "        delta=current-t['InvoiceDate'].max()\n",
    "    return delta.days\n",
    "rfm=pd.DataFrame()\n",
    "g=data.groupby('CustomerID')\n",
    "current=pd.to_datetime('2012-01-01 00:00:00')\n",
    "rfm['Recency']=g.apply(build_R,current)\n",
    "rfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4dd17d",
   "metadata": {},
   "source": [
    "#### 构建F值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bafb63bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Recency</th>\n",
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       "      <td>1</td>\n",
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       "      <th>18281</th>\n",
       "      <td>202</td>\n",
       "      <td>1</td>\n",
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       "      <td>29</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency\n",
       "CustomerID                    \n",
       "12346           347          0\n",
       "12347            24          7\n",
       "12348            97          4\n",
       "12349            40          1\n",
       "12350           332          1\n",
       "...             ...        ...\n",
       "18280           299          1\n",
       "18281           202          1\n",
       "18282            29          1\n",
       "18283            25         16\n",
       "18287            64          3\n",
       "\n",
       "[4372 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_F(group_data):\n",
    "    is_refund=group_data['InvoiceNo'].str.startswith('C')\n",
    "    normal=group_data[~is_refund]\n",
    "    refund=group_data[is_refund]\n",
    "    num=normal['InvoiceNo'].unique().shape[0]-refund['InvoiceNo'].unique().shape[0]\n",
    "    return num if num>0 else 0\n",
    "rfm['Frequency']=g.apply(build_F)\n",
    "rfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64ae9b5",
   "metadata": {},
   "source": [
    "#### 构建M值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0ee25b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
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       "      <th>12347</th>\n",
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       "      <td>97</td>\n",
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       "      <th>12350</th>\n",
       "      <td>332</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18280</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>180.60</td>\n",
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       "      <th>18281</th>\n",
       "      <td>202</td>\n",
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       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>176.60</td>\n",
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       "    <tr>\n",
       "      <th>18283</th>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>2094.88</td>\n",
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       "      <th>18287</th>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>1837.28</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetory\n",
       "CustomerID                              \n",
       "12346           347          0      0.00\n",
       "12347            24          7   4310.00\n",
       "12348            97          4   1797.24\n",
       "12349            40          1   1757.55\n",
       "12350           332          1    334.40\n",
       "...             ...        ...       ...\n",
       "18280           299          1    180.60\n",
       "18281           202          1     80.82\n",
       "18282            29          1    176.60\n",
       "18283            25         16   2094.88\n",
       "18287            64          3   1837.28\n",
       "\n",
       "[4372 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_M(group_data):\n",
    "    money=group_data['monetory'].sum()\n",
    "    return money if money>0 else 0\n",
    "rfm['Monetory']=g.apply(build_M)\n",
    "rfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93458ed0",
   "metadata": {},
   "source": [
    "#### 客户等级评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "45b5f85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#为了方便评估，R值越小价值越大，这里将R值取反处理\n",
    "rfm[\"Recency\"]*=-1\n",
    "R_bar=rfm[\"Recency\"].mean()\n",
    "F_bar=rfm[\"Frequency\"].mean()\n",
    "M_bar=rfm[\"Monetory\"].mean()\n",
    "\n",
    "\n",
    "def level_evaluate(c):\n",
    "    s=\"重要\" if c[\"Monetory\"]>=M_bar else\"一般\"\n",
    "    if c[\"Recency\"] >=R_bar and c[\"Frequency\"]>=F_bar:\n",
    "        s+=\"价值\"\n",
    "    elif c[\"Recency\"]<R_bar and c[\"Frequency\"]>=F_bar:\n",
    "        s+=\"保持\"\n",
    "    elif c[\"Recency\"]>=R_bar and c[\"Frequency\"]<F_bar:\n",
    "        s+=\"发展\"\n",
    "    elif c[\"Recency\"]<R_bar and c[\"Frequency\"]<F_bar:\n",
    "        s+=\"挽留\"\n",
    "    else:\n",
    "        print(c)\n",
    "    s+=\"客户\"\n",
    "    return s\n",
    "\n",
    "\n",
    "rfm[\"type\"]=rfm.apply(level_evaluate,axis=1)\n",
    "# rfm\n",
    "# 统计每种客户的数量\n",
    "v=rfm[\"type\"].value_counts()\n",
    "plt.figure(figsize=(20,8),dpi=100)\n",
    "# x轴刻度旋转45度，为了让字体更加的清晰\n",
    "plt.xticks(rotation=45)\n",
    "ax=sns.countplot(rfm[\"type\"],order=v.index)\n",
    "ax.set_ylabel('数量',size=20)\n",
    "ax.set_xlabel('客户类型',size=20)\n",
    "#在图像上绘制数值\n",
    "for x,y in enumerate(v):\n",
    "    text=ax.text(x,y+1,y)\n",
    "    #文本数值居中对齐\n",
    "    text.set_ha(\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c9ced6",
   "metadata": {},
   "source": [
    "#### 结论/建议"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec3b8b1",
   "metadata": {},
   "source": [
    "从以上图形可以得出各种客户的类型，其中重要价值客户29人，重要发展客户40人，针对不同的客户类型推出不同的\n",
    "营销策略，例如，重要价值客户可积极与其交流沟通，过年过节定期为客户送点小礼品，增加客户的忠诚度，挖掘\n",
    "重要发展客户，将其尽可能的发展为重要价值客户。针对挽留客户，及时推送优惠券，将客户挽留住。\n",
    "\n",
    "注意：不过，通过RFM模型，我们会将用户划分为很多类别，并且，划分的方式可能会具有很多的主观性，那么，\n",
    "如何能够减少客户类别，并具有一定的客观性呢？我们可以通过聚类算法来实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11eb880",
   "metadata": {},
   "source": [
    "### K-means算法 挖掘客户类型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079ee733",
   "metadata": {},
   "source": [
    "聚类属于无监督学习，即训练数据中是不含有标签的。聚类的目的是根据样本数据内部的特征，将数据划分为若干个\n",
    "类别，每个类别就是一个簇。结果为，使得同一个簇内的数据，相似度较大，而不同簇内的数据，相似度较小。聚类\n",
    "也称为“无监督的分类”。其样本的相似性是根据距离来度量的。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a8d451",
   "metadata": {},
   "source": [
    "K-Mean算法，即 均值算法，是最常见的一种聚类算法。顾名思义，该算法会将数据集分为K个簇，每个簇使用簇内\n",
    "所有样本的均值来表示，我们将该均值称为“质心”。具体步骤如下：\n",
    "1. 从训练样本中选择K个样本作为初始质心。\n",
    "2. 计算每个样本到各个质心的距离，将样本划分到距离最近的质心所对应的簇中。\n",
    "3. 计算每个簇内所有样本的均值，并使用该均值更新簇的质心。\n",
    "4. 重复步骤2与3，直到达到以下条件之一结束：质心的位置变化小于指定的阈值。达到最大迭代次数。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2933e0f",
   "metadata": {},
   "source": [
    "#### 优化目标"
   ]
  },
  {
   "attachments": {
    "kwl.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "194b95d6",
   "metadata": {},
   "source": [
    "KMeans算法的目标就是选择合适的质心，使得在每个簇内，样本距离质心的距离尽可能的小。这样就可以保证簇内\n",
    "样本具有较高的相似性。我们可以使用最小化簇内误差平方和（within-cluster sum-of-squares ）来作为优化\n",
    "算法的量化目标（目标函数），簇内误差平方和也称为簇惯性（inertia）。\n",
    "![kwl.png](attachment:kwl.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d79383",
   "metadata": {},
   "source": [
    "#### 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "28e76624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>347</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>332</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetory\n",
       "CustomerID                              \n",
       "12346           347          0      0.00\n",
       "12347            24          7   4310.00\n",
       "12348            97          4   1797.24\n",
       "12349            40          1   1757.55\n",
       "12350           332          1    334.40"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出RFM的值\n",
    "x=rfm.iloc[:,:-1]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "40026f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>2.251129</td>\n",
       "      <td>-0.542685</td>\n",
       "      <td>-0.231399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>-0.898765</td>\n",
       "      <td>0.566089</td>\n",
       "      <td>0.293102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>-0.186869</td>\n",
       "      <td>0.090900</td>\n",
       "      <td>-0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>-0.742733</td>\n",
       "      <td>-0.384288</td>\n",
       "      <td>-0.017516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>2.104849</td>\n",
       "      <td>-0.384288</td>\n",
       "      <td>-0.190705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18280</th>\n",
       "      <td>1.783033</td>\n",
       "      <td>-0.384288</td>\n",
       "      <td>-0.209421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18281</th>\n",
       "      <td>0.837090</td>\n",
       "      <td>-0.384288</td>\n",
       "      <td>-0.221564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18282</th>\n",
       "      <td>-0.850005</td>\n",
       "      <td>-0.384288</td>\n",
       "      <td>-0.209908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283</th>\n",
       "      <td>-0.889013</td>\n",
       "      <td>1.991655</td>\n",
       "      <td>0.023535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287</th>\n",
       "      <td>-0.508685</td>\n",
       "      <td>-0.067496</td>\n",
       "      <td>-0.007813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recency  Frequency  Monetory\n",
       "CustomerID                               \n",
       "12346       2.251129  -0.542685 -0.231399\n",
       "12347      -0.898765   0.566089  0.293102\n",
       "12348      -0.186869   0.090900 -0.012686\n",
       "12349      -0.742733  -0.384288 -0.017516\n",
       "12350       2.104849  -0.384288 -0.190705\n",
       "...              ...        ...       ...\n",
       "18280       1.783033  -0.384288 -0.209421\n",
       "18281       0.837090  -0.384288 -0.221564\n",
       "18282      -0.850005  -0.384288 -0.209908\n",
       "18283      -0.889013   1.991655  0.023535\n",
       "18287      -0.508685  -0.067496 -0.007813\n",
       "\n",
       "[4372 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# 因为R值和M值相对于F值较大，采用欧式距离(欧几里得度量)计算不准确。因此先进行数据的标准化处理，以消除\n",
    "# 不同量纲对距离计算的影响。\n",
    "s=StandardScaler()\n",
    "x_scale=s.fit_transform(x)\n",
    "x_scale=pd.DataFrame(x_scale,columns=x.columns,index=x.index)\n",
    "x_scale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed07520a",
   "metadata": {},
   "source": [
    "#### 肘部法则(拐点法)确定K的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "08bf6da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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da8TNDSZM0Dk7ihCijJKCKad8fa1EReXxzTdqvvtODlsWQhQ9KZhy7PXX86hVy8L48VpMJmenEUKUNVIw5ZhWC5MnG/j9dxUrV2qcHUcIUcYUScFYrVZ+/vnnongqUcLatTPTqlU+s2ZpuXhRBvyFEEXnrgomLy+PAQMGFLg8ceJE++X8/HzeeOONIg8nip9CAVOmGMnJgZkzZbZlIUTRuauC0Wg0BbZQlEoliYmJBW5Xq+XM8NIqKMhC794mYmM1HD8ue02FEEXjrr5NFAoFSuWNu6rVajSagvvsb77dEbGxsbRu3ZonnniCoUOHkpOTg8ViYcaMGYSGhtK+fXuOHTtmv39aWho9evQgNDSUqKgoDAaD/bb4+HjCwsIICwsjLi6uUHnKq1GjjPj4WHnnHS1Wq7PTCCHKgjtudjzxxBO4u7ujUCi4evUqTz/9tP22v18ujBMnTvD555+zYcMGtFotI0eOJCYmhmrVqrFnzx42b95MRkYGQ4cOZePGjWg0GoYMGUKrVq34/PPPee+991i8eDHDhw/n+PHjTJo0iVWrVuHv70/37t0JCQmhTp0695SxvPDxgdGj8xg9WsfGjWqefz7f2ZGEEKXcHQtm0aJF6HQ6lEolr776KosXLwZsg/p/vzxw4ECHXzw5OZmgoCC8vb0BeOyxx0hKSuKHH36gb9++VKpUiUqVKvHQQw+RlJRE1apVSUlJ4YsvvkChUBAZGUnHjh0ZPnw4iYmJhIeHExwcDECnTp3YtGkTw4YNczhXedWrl4mVKzVMnKilTZt8dHIOphDiHtyxYBo3bsx3331HjRo1UKlUBAUFAWCxWFAoFPbLhRUUFMTEiRP53//+R+XKlVm/fj29evVi1qxZhISE2O8XEBBASkoKeXl51KlTB63WNoeWj48Pubm56PV60tPTadq0qf0x/v7+HDp0yKE8Vap43dP7qVbN+54eX1wcybVoETz9NHz6qTdjxxZjKMrG51WSJJdjJJdjiiPXv47Mb9iwgYMHD5KVlcWAAQOIiIigZcuWPPDAA/b7WCwW8vMd36VSq1YtWrRowfDhw3Fzc0Or1dK2bVtmzJhh36oBcHd3Jzs7G6vVSoUKFQo8h06nIzs7G4vFUuA2Dw8Psh2cLvjixRwslsINQFSr5k1mputNT+xorkcfheee0zFtmprnn8/l/vuLZ0CmrHxeJUVyOUZyOaawuZRKxR1/Mf/Xkfn58+eze/duNm3aRIUKFfj000/57bffWLNmjf0++fn5+Pr6OhwuISEBtVpNYmIi69evp23btowZMwYfH58C5WAwGFAqlfj4+HD16tUCz2EwGFCpVLfcptfrC33gQXk3YYIRsxmmTpXZloUQhXdX38Bbtmyha9eu6PV61q1bR2hoaIHb3dzc+Oqrrxx+8QMHDvDwww/bL9evX5+DBw/SqFEj9u/fb7/+8OHD+Pn5ERQUxJkzZ8jKygLg1KlTmEwmfHx8aNSoEQcOHLA/5siRI9SoUcPhTAJq1bIycGAe69ZpSEqSkhZCFM5td5Hl5OQQExODVqvlypUrREREUKVKFRYtWnTLfc1mM0ajkVGjRjn04rVq1SIhIYHGjRujUqlYvHgxQUFBdO7cmSFDhuDn58eJEydISUmhefPm6HQ6wsPDiY6Opm/fvixcuJCIiAhUKhVt2rRhzpw5rFq1iurVq7N69WqWLFni+CciABg6NI/VqzWMG6dj06ZryMagEMJRty0Yi8VCVlYWOp0OtVqNQqGwj4P8XX5+Pnl5eQ6/eI8ePUhOTqZfv37k5OTQoEED3n33XQICApgwYQIxMTG4ubmxbNky+/jKmDFjWLBgARMnTqRBgwb2UvPy8mLFihXMnTuXzMxMoqOjadKkicOZhI2XF7zzjpHBg91Zu1ZNt25y2LIQwjEK6z81xt/k5eXRoUMHatWqRe3atXnqqado1KhRSeQrUTLIX5DFAs8950FamoIffsjF694OsiuyXMVJcjlGcjmmrOW650F+sI2xLFq0iM6dO6PVasnMzHQ4iCh9lEqYOtXA+fNK5s+XecqEEI65qwnEjh8/ziOPPELt2rVve/a+1WpFoZDZeMuaxo0tdOliYulSN3r2NPHAAzKPjBDi7txxCyY9PR2j0cjw4cOxWCz26w0GA1arlby8PLKzs7l8+TIvv/xyocZhhOsbN86IWg0TJ8phy0KIu3fHLZhnnnkGjUaDXq+nTZs2VK9encWLF9OsWTP71kr9+vUJDg6mXr16uLnJbpSy6L77rLz1Vh7TpmnZtctEq1ZmZ0cSQpQCd9yCqVevHj///DN16tRhzZo1VKpUCYPBQEhICPv372f//v2MHTuWHTt2MHLkyJLKLJygf/88ata08M47WgoxaYMQohy6Y8GYTCYuX74MQPXq1e3Tt6jVakwmEwqFAh8fHyZPnoynp2fxpxVOo9PBpElGfvtNxSefyPLKQoh/d8eC+eOPP+jUqRMnT57k8ccfZ9u2bSgUCqxWKxs2bOCJJ55g5syZVK9evaTyCieKiMinRYt8Zs7UcumSs9MIIVzdHQvm//7v/9i+fTsPPvgge/bsoUWLFvajxV577TW2bNlC48aN6d27N7/88ktJZRZOcn155atXYfZsGfAXQtzZHQvm/PnzxMXFkZuby9GjR+1HiaWnp7No0SLi4uJQKpUMHjyY6dOnl0hg4Vz/938WXn3Vtm7Mr7/K/DFCiNu74zdE27ZtOXz4MNnZ2SxatIiMjAw0Gg0tWrTgypUrXLp0iczMTDp27MihQ4c4d+5cSeUWTjR6tBFvbxg3TpZXFkLc3h0PU75+ZFhOTg7dunVj9+7d+Pj4MHXq1Fvu+9xzz6FW39V5m6KUq1zZVjLR0Tq2bFHz7LNyWJkQ4lZ3tY/jzTffZP78+Xh4eNz2ZMqZM2dStWrVIg0nXNerr5oICjIzfrwWo9HZaYQQruiOmxxdu3a1L0+sUCjYs2cPe/fuveV+JpMJk8nEl19+WTwphctRq20D/p07e/DBB24MHSqzOAghCrpjwYwYMQKtVovRaOS7777jkUceoVatWgXuY7Va7QUjypdWrcyEh5uYN8+Nrl1N+PrKgIwQ4oY7FkzTpk0ByMzM5OzZs3z33XdotVo2bNhQIuGE65s40UjLlmqmTdPy/vsGZ8cRQriQO47BmM1mvvzyS6pVq8aCBQuYNGkSq1evJjc3F8A+2J+WlsYHH3xQ/GmFy3nwQSv9++exZo2Gn36Sw5aFEDf86zfCxx9/jNlsm9zw7bffxtPTk/DwcAB27twJwDfffMPPP/9cfCmFS3vrrTyqV7fw9ts6bpp0WwhRzt2xYFQqFbm5ubz00kukpaXh9deShtf/6+npyalTp4iNjZXJLssxLy/blP4HD6r46is5VF0IYXPHgsnOzubKlSu8+eabBAQEoFTa7q5UKtmyZQtZWVkMGTKEOXPm8NBDD5VIYOGaunTJp0EDM1OmaMnJcXYaIYQruG3BXLhwgeeff56AgABat27N77//DkBeXh4Wi4Xdu3dz5coVPD09CQgIKLHAwjUplTBtmoFz55QsXCjrAgkh7lAwVatWJTo6mitXrrBy5UpGjx7NuXPnWLRoERcuXOCtt96iZs2adOvWjV69enHhwoWSzC1cUNOmFl5+2cSSJW6kpsry2UKUd3fcRfbMM8/QrVs3Nm3axIIFC2jVqhX79u2je/fuDBgwgJycHF588UU6derEuHHjSiqzcGHvvGNEpYJJk2S2ZSHKuzsWjEKhYPPmzSxYsICaNWsSHR1N06ZNGTx4MJ9++il9+/YF4LXXXqNKlSq3nUZGlB81algZOjSPxEQNe/aonB1HCOFEdywYpVLJn3/+SadOncjMzCQpKQmtVsvp06dp3rw5S5cu5dlnnyUyMpIuXbrg5ib73gUMHJhHQICFceNkeWUhyrPbFozZbGbdunVUqlSJLVu2cOHCBfbs2YNKZfut9LnnnmPNmjUsXLgQf39/Pv7443sKEhkZyfLlywGwWCzMmDGD0NBQ2rdvz7Fjx+z3S0tLo0ePHoSGhhIVFYXBcOPs8fj4eMLCwggLCyMuLu6e8ojCc3e3neH/yy8qPvtMllcWory6bcFkZmYSExNDfn4+bm5u/PDDD/z2229s3ryZOXPm8Mcff/DLL7/g5+dH69at7dPKFMaXX35JZmYmr776KgCxsbHs2bOHzZs3M3v2bKKiouxznQ0ZMoSmTZuyb98+/Pz8WLx4MQDHjx9n0qRJxMTEkJCQwPLly0lOTi50JnFvnn8+nyefzGfGDDeuXHF2GiGEM9y2YO677z42bNhAkyZNGDFiBL1796Z///4EBwfTsWNHWrduTVxcHE8//TSnT5+me/fuhQqQkZHBrFmzmDRpkn09mYSEBPr27UulSpUICgrioYceIikpieTkZFJSUhg0aBAKhYLIyEgSExMBSExMJDw8nODgYLy9venUqRObNm0qVCZx764vr3zlioI5c2TAX4jy6I6nXXt5eTF37lwmTJhAVlYWJpMJPz8/2rVrZ7/P6dOnGTp0KCkpKbz77rsOB5g8eTIqlYply5Zx//338/rrr5Oenk5ISIj9PgEBAaSkpJCXl0edOnXsSwj4+PiQm5uLXq8nPT29wFaUv78/hw4dcihLlSpeDue/WbVq3vf0+OLirFxPPQX9+sFHH7kxbJgb//d/rpHr30gux0gux5SnXP86r4dCoWDy5MkAhIaGUr9+/QK316xZk1WrVpGVleXwix86dIht27bx2muv8eSTTxIXF0ePHj0wmUx4e994s+7u7mRnZ2O1WqlQoUKB59DpdGRnZ2OxWArc5uHhQXZ2tkN5Ll7MwWIp3JTz1ap5k5np2OuVBGfnGjZMwZo1nrzxhpkvvtCjULhGrtuRXI6RXI4pa7mUSsUdfzF3aPpbb29vqlevfsv1Hh4e3H///Q6H279/P8HBwURHR9OqVSvmzp3LpUuXMBgMBcrBYDCgVCrx8fHh6tWrBZ7DYDCgUqluuU2v19unthHOU7WqlZEjjezcqWbrVjlsWYjyxKnfwH+fZkalUqFWq2nUqBH79++3X3/48GH8/PwICgrizJkz9q2lU6dOYTKZ8PHxoVGjRhw4cMD+mCNHjlCjRo2SezPitnr3NlGnjpl33tEhp0oJUX44tWBatmzJwYMHSUlJAWDVqlV4e3szdOhQlixZwq5du1ixYgUpKSk0b94cnU5HeHg40dHR/PTTT0yaNImIiAhUKhVt2rQhKSmJVatWsW3bNlavXk379u2d+fbEXzQa24B/SoqSDz+Uw5aFKC8UVqvVqevcbtu2jffff5+MjAzuu+8+pk2bRkhICFu3bmX58uW4ubkxfPhw+9iPwWBgwYIF7N27lwYNGjBq1Cj78gG///47c+fOJTMzk169etGxY0eHssgYTPH6z3/cyctTsXy5BZVKhVKZj6enEbPZdRaRcaXP62aSyzGSyzHFNQbj9IJxJVIwxSs9XUVmpjuRkQpSUyEwEOLjLdSsqXeZknGlz+tmkssxkssxLjHIL8S9qFTJzV4uAKmp8OKLSnJz5TwZIcoiKRhRYkwmpb1crktNhfx8+WcoRFkkP9mixGg0FgIDC14XGAh6veylFaIskoIRJcbT00h8/I2SCQyEjz+20qePihEjtPztFCchRCknBSNKjNlsG9DfvTufU6dg9+586ta9RsOGeXz2mYZmzTzZuPFfJ5cQQpQSUjCiRJnNFnQ6PYGBoNPpcXOzMHmykS1brlG1qpXevd3p3VvHuXOy5LIQpZ0UjHAJDRpY+Pbba4wbZ2T7djXNm3sSG6vB4hpHLwshCkEKRrgMjQaGDs1j585cHn3UzIgROjp2dOePP2RrRojSSApGuJwHH7Syfr2eefMM/PKLitatPZk3z03mMROilJGCES5JoYCePU3s2ZNLu3b5vPuulmee8eCnn+SfrBClhfy0Cpfm62vlo48MxMZe48oVBc8+68G4cVpycpydTAjxb6RgRKkQHm5mz55cXnvNxLJlbrRs6cn27bK+jBCuTApGlBre3jBzppGEhGt4eFjp3t2DgQN1XLggBwEI4YqkYESp8/jjZrZvv8aIEUY2bFDTvLkHa9eqkXnBhXAtUjCiVNJqYdSoPLZvv8aDD1oZPNidLl3cSU2VrRkhXIUUjCjVgoIsJCZeY8YMA0lJKlq18iQmRkN+vrOTCSGkYESpp1RCnz62Q5pbtDAzYYKOZ5/14OhR+ecthDPJT6AoM/z8rMTG6vnoIz1//qmgbVsPpk51Q693djIhyicpGFGmKBTQoUM+e/fm0rWrifff1xIW5smePXJIsxAlTQpGlEmVKsH8+Ua++uoaViu89JIHb72l5coVZycTovyQghFlWosWZnbtymXIECNr1tjWnNmwQQ5pFqIkSMGIMs/dHd55J49vv71GjRpW+vZ159VXdZw9K4c0C1GcpGBEufHooxY2b77GxIkGdu2yrTmzYoWsOSNEcXGZgrlw4QJPPPEEZ86cwWKxMGPGDEJDQ2nfvj3Hjh2z3y8tLY0ePXoQGhpKVFQUBoPBflt8fDxhYWGEhYURFxfnjLchXJxaDYMGmdi1K5fGjc2MGaOjQwd3zpxRYzC4k5oKBoM7KpXL/GgIUWq5zE/RuHHjuHTpEgCxsbHs2bOHzZs3M3v2bKKiojCZTAAMGTKEpk2bsm/fPvz8/Fi8eDEAx48fZ9KkScTExJCQkMDy5ctJTk522vsRrq1WLStr1+pZuFBPhQoqLl3S0aKFmlq1oEULNadPS8kIca9c4idozZo1GAwGatSoAUBCQgJ9+/alUqVKBAUF8dBDD5GUlERycjIpKSkMGjQIhUJBZGQkiYmJACQmJhIeHk5wcDDe3t506tSJTZs2OfNtCRenUEDXrvksW2YhMlJBaqrt+tRUePFFJbm5WucGFKKUc3rBpKam8sEHHzBz5kwUCtuga3p6OiEhIfb7BAQEkJKSwtmzZ6lTpw5are0H38fHh9zcXPR6/S2P8ff3JyUlpWTfjCiVrNYb5XJdaiqkp6v4/nuVHHEmRCGpnfni+fn5jBw5kujoaHx9fe3XWywWvL297Zfd3d3Jzs7GarVSoUKFAs+h0+nIzs7GYrEUuM3Dw4Ps7GyH8lSp4lXId2JTrZr3v9/JCSTXnZnNEBhIgZIJDISMDAWdOnkQFARvvAGvvAJ/++dXolzl8/o7yeWY8pTLqQWzdOlS6tatS9u2bQtc7+PjQ3Z2tr10DAYDXl5e+Pj4cPXq1QL3NRgMqFSqW27T6/UolY5toF28mIPFUrhfV6tV8yYz07FCKwmS69+5uSmJj3fnxReVpKbayiU+3oKvr56FC5WsXOnGkCEqxoyx0rmzid69TQQHl+yhZ670ed1McjmmrOVSKhV3/MXcqbvINm/ezPbt22nWrBnNmjUjPT2dTp06kZeXx/79++33O3z4MH5+fgQFBXHmzBmysrIAOHXqFCaTCR8fHxo1asSBAwfsjzly5Ih9TEeIOzGbLdSsqWf37nxOnYLdu/OpWVOPWm2ha9d8Nm++xtatuXTokM+aNRpatfLkhRfc+fprNX8deyKE+AdO3YLZuHFjgctPPfUUsbGxZGZmMmTIEPz8/Dhx4gQpKSk0b94cnU5HeHg40dHR9O3bl4ULFxIREYFKpaJNmzbMmTOHVatWUb16dVavXs2SJUuc9M5EaWM2W9Dp9H/9JqfHbC54e/36FhYsMDBhAqxerWHlSjf69XPH19dCr14mXnnFxH33yWCNEDdz+iD/P2nYsCETJkwgJiaGnTt3smzZMvv4ypgxYwgMDGTixIkEBAQQHR0NgJeXFytWrGDXrl0sWbKE6OhomjRp4sy3IcqgypXhjTdM/PhjLqtWXePRRy3MnetGo0ae9O2r47//lYMChLhOYbXKj8N1MgZTcspSrpQUBZ984saqVRquXFEQFGSmd28TnTub8Lq340buKVdJkFyOKWu5XHoMRoiy4IEHrEycaOTw4RwWLNCj1cLo0Trq1fMiOlrL77/Lj5kon+RfvhBFxN0dunfP59tvr7F5cy7PPpvPp59qaN7ck5dfdicxUS1LOYtyRQpGiCKmUEDjxhYWLzZw6FAu48YZOXVKSZ8+7jRu7MncuW5kZMhMzqLsk4IRohhVrWpl6NA89u/PJTb2Gg8/bGHmTC2NGnnSv7+OH36QgwJE2SUFI0QJUKkgPNzM2rV69u3LoU8fE9u3q+nQwYOnnvIgNlZDbq6zUwpRtKRghChhDz1kZcoU20EBc+falpsYMUJH/fpejBun5cSJG7vPVCqlLCMgSi351yqEk3h6Qq9eJr777hoJCddo0yafjz/W8MQTXnTu7M7+/RpSU91lGQFRasm/VCGcTKGAxx83s3SpgZ9/zmXMGCPJyUry8nR07KiUZQREqSUFI4QLqV7dSlRUHklJudSta/nHZQSMRvmxFaWD/EsVwgWp1VChgoXAwILXBwbC0aMKXn7ZndhYDRcvyuHOwnVJwQjhojw9jcTH3yiZwED48ksLp0+b+PNPJSNG6AgJ8aRLF3c+/1zD5cvOzSvE3zl1NmUhxO3dWEZAi8WiRqnMx9PTSGCghe7djRw/ruTrr9V8/bWGt97SMXKkllatzLzwgolnn82nYkVnvwNR3knBCOHCbreMgEIBISEWQkLyGDs2j6NHlcTHq9mwQcPQoe5oNFZatzbToYOtbLxdcxFFUcZJwQhRyikUUK+ehXr18njnnTwOHVISH69hwwY1337rjlZrpXXrfF54IZ927fKLbIZnIf6NFIwQZYhCAQ0bWmjY0MiECUYOHlSyYYOtbLZs0aDTWXn6aVvZPPNMPp6ezk4syjIpGCHKKKUSmja10LSpkUmTjOzfr2LDBjUbNqjZuFGDu7uVZ56xlc3TT+fj4eHsxKKskYIRohxQKiE01ExoqJkpU4z8+KOKr79Wk5BgG7fx8LDSrl0+HTrYykanc3ZiURZIwQhRzqhU8OSTZp580sz06Ub++19b2WzcqCYuToOXl61sXnjBROvWZrQycYAoJCkYIcoxlQpatDDTooWZGTOM7Nmjsu9C++orDd7eVp591lY2rVqZcXO7/jjblDWpqaBUuuPpacRstjj3zQiXIydaCiEA2+wBYWFm3nvPyLFjOaxZc43nnstnyxY1PXt68MgjXgwbpuPnn2USTnF35F+EEOIWGg089ZSZ99838MsvOXz++TXats0nMVFNbq5MwinujuwiE0LckZsbPPOMmWeeMWMwwNWrnqSmFvzdNDUVzp5VsXatG/XqWWjQwMz991tRyFRp5ZoUjBDirtmOLrMQGKgsMNNzYCBcvgwLFrhhNttapVo1C/XrW6hf30yDBmbq17dw332yPnR5IgUjhHCIbRJOJS++aCuZwECIj7fNm3bypIXjx5UcPqzi0CEVR44o+e47NywWW+n4+lpo0MBWOrY/FqpXl9Ipq5xeMOvWrWP+/PlcuXKF0NBQZs+ejY+PD7NmzSI+Pp5q1arx7rvvEhISAkBaWhqjR4/m5MmTPPnkk0yfPh3dXwftx8fHM3/+fACGDRtGx44dnfW2hCizbjcJp9lswd0dmjSx0KSJBTABkJsLx47ZyubQIRWHDyv59ls3rFZb6dSoYaFePTMNGth2rdWrZ6FqVSmdssCpBXPkyBFiYmKIiYkhMDCQIUOGsGTJEvz9/dmzZw+bN28mIyODoUOHsnHjRjQaDUOGDKFVq1Z8/vnnvPfeeyxevJjhw4dz/PhxJk2axKpVq/D396d79+6EhIRQp04dZ75FIcqk203C+U88PW0rdj7+uJnrpZOTYyudQ4eU9i2dLVs09sf4+1vsWzjXt3YqVy7mNyWKnFMLRqPRMH/+fOrVqwdAWFgY+/fv5+eff6Zv375UqlSJSpUq8dBDD5GUlETVqlVJSUnhiy++QKFQEBkZSceOHRk+fDiJiYmEh4cTHBwMQKdOndi0aRPDhg1z5lsUQvwDL68bMwtcL52rV+HoUVvpHDli28W2ceON0qlZ80bp2LZ0zPj4FHxeOT/HtTi1YK6XwXU7duygVatWrFixwr5LDCAgIICUlBTy8vKoU6cO2r9OLfbx8SE3Nxe9Xk96ejpNmza1P8bf359Dhw6VyPsQQty7ChWgWTMzzZrdKJ2sLOxlc/iwbWwnIeFG6dSqdaNswsKgYkU3Xnrp+tiQmvh4JTVr6qVknMTpYzDXbd68mZSUFJYuXcpHH32E900LWLi7u5OdnY3VaqVChQoFHqfT6cjOzsZisRS4zcPDg+zsbIcyVKlyb/OYV6vmmotuSC7HSC7HFGeuatWgdm146aUb1126BAcP2v4kJSlJSrItT9CgAfTuzS3n52zZ4olabTsYQaP5x5cpUeXp79ElCiYtLY2JEycyb948PD098fHxITs7G19fXwAMBgNeXl74+Phw9erVAo81GAyoVKpbbtPr9SiVjp1HevFiDhZL4QYXbfuiHSu0kiC5HCO5HOOsXA0a2P5ERtouX7yowGLx+MfzczIyICwMlEor/v5WAgMt1KplITDQ+td/bX/+vrutOJS1v0elUnHHX8ydXjBXr15l4MCB9O3blyeffBKARo0asX//fmrXrg3A4cOHeeWVVwgKCuLMmTNkZWVRsWJFTp06hclkwsfHh0aNGrFz50569uwJ2A4gqFGjhtPelxCi5FSpYsVg+Ofzc6pVM/P++3mcOqXk1CklqalKNm9Wc+FCwTLy8bm5fAoWkJ+fFZWqhN9UGeDUgjGZTAwYMICQkBD69etnv75z584MGTIEPz8/Tpw4QUpKCs2bN0en0xEeHk50dDR9+/Zl4cKFREREoFKpaNOmDXPmzGHVqlVUr16d1atXs2TJEie+OyFESbrd+Tn33WegW7dbx2ByciA19XrpKOzlc/Soik2b1JhMN6YhUKutBAT8cwHVqmX511VCy+vBBwqr1eq0A86/++47Bg4cSKVKlVD99euBv78/X3zxBVu3bmX58uW4ubkxfPhw6tevD9h2iS1YsIC9e/fSoEEDRo0ahddff7u///47c+fOJTMzk169ejl8HozsIis5kssxkuvuXP8i//v5OY4ym+HsWcU/FlBqqpLLlwvOgVO1qq1wbi6gWrWsf239KDhzxv0fT0x1lZIprl1kTi0YVyMFU3Ikl2Mkl2OKO1dWFvaySUmxFdD1MvrzT4V9uhyAuDgrb76puGXXXWKimYwMA76+VqpWde4uuDI7BiOEEKVNxYpQr56FevVu3QIxmeDMGYW9gGrW1BYoF7AdfHDxooo2bTwB2wEIVataue8+K76+Vnx9bVPo2P7fdtnX10r16lb7mjylgRSMEEIUIY0GHnjAygMPmAEzBoOawED1LVswvr5mVq7MIyNDcdMfJefOKTh8WM2FCwr7HG43q1Lln8vnvvusf11vu+zu/u9Zi3tsSApGCCGK0e0OPqhe3UBExO2/zPPzbYdfnzt3o3z+Xka//67k/Hk1+fm3FlGFCjfK5u9l5Otr5YEHwGK5sbZPcZyYKgUjhBDF6E6Tg96JWo29DP56pn+8n8UCly7dKJ7z529sCV0vogMHlJw/r8BguFFE69fDW2/demLq7t1adDp9Ubx1KRghhChujkwO6iilEqpWtY3hPPLI7e9ntdrme7tePnXq6P7xxNT8/KJb6FgKRgghygGFwnZwQsWKFurW5bYnpqrVRTcGU3RVJYQQotSwjQ1ZCAy0Xb4+NuTpaSyy15AtGCGEKIcKOzbkCNmCEUKIcur62FBgIOh0RT+zgBSMEEKIYiEFI4QQolhIwQghhCgWUjBCCCGKhRxFdhOl8tbpFkry8cVFcjlGcjlGcjmmLOX6t8fIdP1CCCGKhewiE0IIUSykYIQQQhQLKRghhBDFQgpGCCFEsZCCEUIIUSykYIQQQhQLKRghhBDFQgpGCCFEsZCCEUIIUSykYIrAb7/9Rvv27Z0do4B169bRrFkzHnnkESIjI7l06ZKzIwFw8uRJoqKi6NWrF++//z55eXnOjlTAhQsXeOKJJzhz5oyzowAwbNgwHn/8cZo1a0azZs1YsGCBsyMVEBkZyfLly50dA4D4+Hj753T9z8MPP8zZs2edHY3Y2Fhat27NE088wdChQ8nJyXF2JAA+/PBDwsPDadmyJePHjyc3N7doX8Aq7snhw4etrVu3trZu3drZUeyuZzp8+LD1ypUr1l69elmnTJni7FjWnJwc61NPPWXduHGj9eTJk9ZevXpZFy1a5OxYBfTv399at25da1pamrOjWK1Wq7V169bWc+fOOTvGP1q3bp21ffv2VpPJ5Owo/2j37t3Wrl27OjuG9Y8//rC2bdvWevXqVavRaLQOHTrUOmvWLGfHsiYmJlojIiKsFy9etBqNRusbb7xhHTt2bJG+hmzB3KMVK1Ywfvx4Z8coQKPRMH/+fOrVq0fFihUJCwtzid/IMzIy6N+/PxERETzwwAM888wznDhxwtmx7NasWYPBYKBGjRrOjgLApUuXMJlM+Pr6OjvKLTIyMpg1axaTJk1CrXa9OXOtVitz585l1KhRzo5CcnIyQUFBeHt74+bmxmOPPeYSW1Vbt26lU6dOVK5cGTc3Nzp06EBSUlKRvoYUzD2aN28etWvXdnaMAoKDg6lXr5798o4dO2jSpIkTE9k8+OCDdOnSBYATJ06wdu1ann/+eSensklNTeWDDz5g5syZKBSuMdvtkSNHMBqNhIWF8dhjjxEVFeUyu1YmT56MSqVi2bJlTJ48mXPnzjk7UgE7duygQoUKNGrUyNlRCAoK4scff+R///sfmZmZrF+/nlatWjk7FkqlkitXrtgv//rrr0X+y5UUzD1ylS+j29m8eTMpKSl0797d2VHsdu3aRY8ePfD09CQ0NNTZccjPz2fkyJFER0e71NaC1Wqld+/efPPNN+zcuZPs7GyWLl3q7FgcOnSIbdu20aFDB7p168alS5fo0aOHS42nffzxx/Tu3dvZMQCoVasWLVq0YPjw4fTv3x83Nzfatm3r7Fh07NiRTz75hHnz5jFz5kw+/PBDXnjhhSJ9DSmYMiwtLY2JEycya9YsPD09nR3HrlWrVuzevZv777+fGTNmODsOS5cupW7dui7xQ3+z1q1bM3DgQLRaLR4eHvTs2ZPdu3c7Oxb79+8nODiY6OhoWrVqxdy5c7l06RK//PKLs6MBcOrUKVJSUmjZsqWzowCQkJCAWq0mMTGR9evX07ZtW8aMGePsWLRo0YLY2FisVis//vgjAQEBRX6wkhRMGXX16lUGDhxI3759efLJJ50dB7D94B87dgwANzc3nnvuOf73v/85OZVtK2/79u32I4/S09Pp1KkTmzZtcmqurVu3FthXf+7cOVQqlRMT2Xh6ehIQEGC/rFKpUKvVeHt7OzHVDRs2bCA8PByl0jW+3g4cOMDDDz9sv1y/fn0OHjzoxEQ31KtXj0GDBnH+/HlGjx5d5P++XONvQBQpk8nEgAEDCAkJoV+/fs6OY3fhwgWGDh3KmTNnMJvNJCYm0rhxY2fHYuPGjezbt4+9e/eyd+9e7r//fr788ksiIiKcmuv48ePMnj2b7OxsTp48ycqVK3n22WedmgmgZcuWHDx4kJSUFABWrVqFt7c3tWrVcm6wv2zbts0lxjiuq1WrFgkJCRw9epRffvmFxYsXExQU5OxYdp9++inBwcGEhYUV+XO73uEf4p7t3r2bgwcPcvLkSZo1awaAv78/X3zxhVNzNWnShMjISP7zn/+Qn59P69atGTZsmFMzubIBAwYwduxYWrZsSeXKlenYsaNLjCsEBAQwefJkhg0bRkZGBvfddx8LFy50ia2rixcvcuLECRo2bOjsKHY9evQgOTmZfv36kZOTQ4MGDXj33XedHQuwfV7Lly9n9erVxfL8smSyEEKIYiG7yIQQQhQLKRghhBDFQgpGCCFEsZCCEUIIUSykYIS4R+np6eTn599yfWZm5h0fs2nTplse99lnn3Hx4sW7fu2oqCinHx0oxO1IwQhxD3JycujZsyfr168HwGw2k5+fz5YtW3j55Ze5evUqYJuO5uYyOXDgAAsXLiwwUWRGRgbTpk3j8uXLd/Xau3btYuPGjcTFxWEymW57v+TkZNatWwfAY489RnJyMhs2bOCHH35Ar9cTHh5OWlqaw+9diH8jBSNEIVksFsaPH09eXh4xMTE0aNCA9u3bs3TpUqZOnUqFChVo0aIFrVq1on379sTFxdkfm5ycTLt27Qo83/bt26lfvz4ajYbU1FRSU1Nvu45PRkYG0dHRTJw4Ea1Wy4QJE7jdGQeVKlXigw8+4Oeff7afdb948WKqVKnCr7/+ypUrV/Dz8yu6D0aIv8iJlkIU0oQJEzAajWzduhWTyUS7du348MMPefPNN+nTpw99+vRh+/btzJ8/n6+++go3NzeOHDlCv379uHbtGmq1mtWrV1OzZk3WrVtHXFwcFy9epH///gBcvnyZl19++ZYp57Oyshg4cCCtWrWie/futGvXjm7dujFs2DBmzZqFTqcrcP/k5GS6dOlCRkYGFouFvXv3EhISwrlz5zh8+DDNmjWzT6uSn5+P1WpFo9GUzIcoyjQpGCEKKSoqiooVK7Jy5UpCQ0OJj4/H19eXOXPmMGzYMJo1a8bTTz9N3bp1cXNzA2xTpHt6evLjjz8C8OOPPzJ16lSOHz/O2bNn2bFjh/2+c+fOveXs+KNHjzJixAiCgoKYOnUq48ePp3Hjxnz22Wf06dOH559/nrfffpvWrVvbH3PkyBFSU1NZsWIFV65c4eeff8bd3Z0DBw6wZ88eMjIyeOqpp+y786KioujRo0dJfISijJOCEaIQrl69yoEDB9BoNHz77bekpqbSokULtFotMTExpKen27c8LBYLubm5TJs2DR8fn1ueS6lUMmPGDF599VV7uYBta8LDwwOwLTX94Ycf8vXXX/P6668zdOhQlEolf/75Jw888ADVq1dn7dq1vPvuuwwYMIBatWoxY8YMGjZsSGRkJAsWLKB27dr88ccfVKxYkYCAAJo3b84HH3zAtm3bCAgIYNKkSfj7+0u5iCIjBSNEIej1eo4dO8aff/5JVlYWVatW5ejRoxw8eJDk5GQ6d+7M4MGDb9ld9euvv/7j8w0bNgyFQsHUqVMZN24cYCsYrVYLgNFo5PTp03z++ecF5tnSarX2NYk8PDyYMmUKXbp0Yc2aNTzyyCMYjUb69euHv78/H330EREREbz00kssWbKEffv2oVQqSU1NJSAggPPnz7vEwnSi7JCCEaIQfH19iYqKYsuWLWRnZ9OuXTsUCgU1a9Zk0aJFrFq1im3btqFUKrFYLOTk5LBlyxYAzp49a/8iN5vN1KxZkyZNmnD+/HnWrl3Lyy+/THBwMEaj0V4wwcHBfP7553eV7dFHH+XRRx+1X37vvfeoVKkSM2bMoGPHjoSEhDB//nx69+5Nr169OHDgAM2bNyctLY0HH3ywiD8pUZ5JwQhxD06fPs3Vq1f59NNPSUxMZO/evfz3v/9l8+bNzJ8/n+bNm98y7X+NGjX47rvvANsYzPTp0wGoXr06zz33HCtWrGD27Nnk5eXdsgVUGD179uTatWtkZmZSo0YN4uLiOHv2LCtXrqRSpUq8+eabvPbaa5w7d466deve8+sJcZ0UjBD34NChQ/Tu3ZuQkBB27NhBXFwcly5dIjIyktOnT7N3716WLl1KTk4OrVu3plOnTnd8vu7du/P9998DcO3atSJZxOubb77hyy+/5Pvvv+f999/n6NGjDBs2jMcee8x+0MHw4cNp3ry5S0y5L8oOKRghCun06dP89NNPzJ49m/T0dDw9PenZsyc9e/YkPj6e1atXk5OTw/z58+27nv5pDObm81fq1atHvXr1ANvhyP9WMHe72saJEyc4ceIE/fv35+zZs3Tp0sV+aPJ//vMfRo8ezSeffHJXzyXE3ZKCEaIQcnNzGTJkCK+99hrff/89O3bsoHLlyuj1ej755BPWrl1LbGwsf/75J6+++iqvvPIK4eHhmEymW8Zg7r///lue/9KlS/zxxx/4+/vfMYfJZMJsNv9r3tGjR9OvXz/69++Pn58fCQkJVK5cmZCQEGJiYnjssceYPn06K1asoGrVqoX7UIT4GzmTX4hCyMrKIigoiL59+3Lx4kUMBgOjRo1iypQp/PTTT6xevRp/f38ef/xxVqxYwYEDB0hMTMTDw4OXXnqJpKQkkpKSWL9+PW3btrU/72+//cYzzzzDoEGDeOONNwgICLhjDpPJ9I/zoN1sz549vPPOO3Tv3p0uXbqwdOlSVqxYwZEjR3j99dcZO3YssbGxBAcH06FDB/bs2VMkn5EQsqKlEEXo5iO/CstkMhXpmfS7du3i8uXLPPvss/ZsOTk5rFq1iq5du1KxYkX7fTdu3MjTTz9dJAcXCCEFI4QQoljILjIhhBDFQgpGCCFEsZCCEUIIUSykYIQQQhQLKRghhBDFQgpGCCFEsfh/ipAB6cN8JoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据最小的SSE原则，来选择最佳的K值，即客户的类别数量\n",
    "# 导入相关的库Kmeans库\n",
    "from sklearn.cluster import KMeans\n",
    "scope=range(1,10)\n",
    "sse=[]\n",
    "for k in scope:\n",
    "    kmeans=KMeans(n_clusters=k)\n",
    "    kmeans.fit(x_scale)\n",
    "#     获取簇惯性，即样本距离聚类中心的最近的距离的总和\n",
    "    sse.append(kmeans.inertia_)\n",
    "plt.xticks(scope)\n",
    "ax=sns.lineplot(scope,sse,color='blue',marker='o')\n",
    "ax.set_xlabel('簇的个数')\n",
    "ax.set_ylabel('簇惯性')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29545542",
   "metadata": {},
   "source": [
    "#### 轮廓系数法确定K值"
   ]
  },
  {
   "attachments": {
    "Snipaste_2021-10-29_19-32-11.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fd2d3acb",
   "metadata": {},
   "source": [
    "![Snipaste_2021-10-29_19-32-11.png](attachment:Snipaste_2021-10-29_19-32-11.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ee68de1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "# 构造自定义函数\n",
    "def k_silhouette(x,clusters):\n",
    "    K=range(2,clusters+1)\n",
    "#     构建空列表，用于存储不同簇数下的轮廓系数\n",
    "    S=[]\n",
    "    for k in K:\n",
    "        kmeans=KMeans(n_clusters=k)\n",
    "        kmeans.fit(x)\n",
    "        labels=kmeans.labels_\n",
    "        S.append(metrics.silhouette_score(x,labels,metric='euclidean'))\n",
    "    plt.style.use('ggplot')\n",
    "    plt.plot(K,S,'b*-')\n",
    "    plt.xlabel('簇的个数')\n",
    "    plt.ylabel('轮廓系数')\n",
    "#     print(S)\n",
    "plt.show()\n",
    "k_silhouette(x_scale,10)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cc6590",
   "metadata": {},
   "source": [
    "#### 执行聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb2df57",
   "metadata": {},
   "source": [
    "结合公司实际业务和人力物力资源，两种客户类型太过单调(只分为重要客户和一般客户),因此采用肘部法则的结果，所以最终将其分为三种客户类型，有利于挖掘客户潜质，从而实现精准化营销。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "19f2e752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-8.62323043e-01  9.31122712e+00  1.09906829e+01]\n",
      " [ 1.52486371e+00 -3.45294122e-01 -1.74431672e-01]\n",
      " [-5.18762088e-01  6.40120503e-02 -4.53102594e-03]]\n",
      "[1 2 2 ... 2 2 2]\n",
      "5519.688812645494\n",
      "16\n",
      "-5519.688812645494\n"
     ]
    }
   ],
   "source": [
    "# init=random 表示使用K-means算法，默认是使用Kmeans++算法\n",
    "kmeans=KMeans(n_clusters=3,init='random')\n",
    "# 训练数据\n",
    "kmeans.fit(x_scale)\n",
    "# 获取聚类后的质心，输出的为三维坐标，因为在训练时，训练的是三个特征，rfm\n",
    "print(kmeans.cluster_centers_)\n",
    "# 获取每个样本所属的簇\n",
    "print(kmeans.labels_)\n",
    "# 获取sse\n",
    "print(kmeans.inertia_)\n",
    "# 获取迭代次数\n",
    "print(kmeans.n_iter_)\n",
    "# 聚类的分值，分值越大，效果越好。\n",
    "print(kmeans.score(x_scale))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5fccb8",
   "metadata": {},
   "source": [
    "#### 数量分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d71b982d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取每种类别的客户的数量\n",
    "v=pd.Series(kmeans.labels_).value_counts()\n",
    "ax=sns.countplot(kmeans.labels_,order=v.index)\n",
    "# 图像显示数值\n",
    "for x,y in enumerate(v):\n",
    "    text=ax.text(x,y,y,fontsize=14)\n",
    "    text.set_ha('center')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108e532e",
   "metadata": {},
   "source": [
    "#### 客户群体分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0790049f",
   "metadata": {},
   "source": [
    "##### 对于聚类算法，只能帮我们划分出客户的群体，但具体是哪个群体，什么类型，需要我们自己分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e6f4b178",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取出RFM的值\n",
    "x=rfm.iloc[:,:-1]\n",
    "# x[kmeans.labels_ == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2313a13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(3,3)\n",
    "fig.set_size_inches(18,15)\n",
    "for i in range(3):\n",
    "    d=x[kmeans.labels_ == i]\n",
    "    sns.boxplot(y='Recency',data=d,ax=ax[i][0])\n",
    "    sns.boxplot(y='Frequency',data=d,ax=ax[i][1])\n",
    "    sns.boxplot(y='Monetory',data=d,ax=ax[i][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c75089",
   "metadata": {},
   "source": [
    "1.第一行的图可知，对于R值较高，F,M值较低的客户群体，属于最近消费的用户(很可能是新客户)，该客户中可能包含\n",
    "很多潜能客户，需要频繁的发送活动消息，增加其消费频率与消费金额。\n",
    "3.第二行的图可知，对RFM值都低的客户群体，属于一般挽留客户，该客户可以不用耗费过多的投入，在促销活动发送\n",
    "通知，给予一定的优惠券激活。\n",
    "2.第三行的图可知，对于RFM三高的用户，属于重要价值客户，主要重点关注(SVIP),给予特别的VIP服务，例及时沟通，\n",
    "过节送礼物，增加客户的忠诚度。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cbd5dd",
   "metadata": {},
   "source": [
    "#### 绘制客户群体散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d88e7c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x279032166a0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig=plt.figure()\n",
    "fig.set_size_inches(12,8)\n",
    "ax=Axes3D(fig)\n",
    "color=['r','g','b']\n",
    "for i in range(3):\n",
    "    d=x[kmeans.labels_==i]\n",
    "    ax.scatter(d['Recency'],d['Frequency'],d['Monetory'],color=color[i],label=f\"客户群{i}\")\n",
    "ax.set_zlabel('Monetory')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Recency')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3884af42",
   "metadata": {},
   "source": [
    "蓝色群体为一般挽留客户，RFM三者都较低，红色群体为潜能客户，R值较高，FM值较低，绿色为重要价值客户。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b8329e",
   "metadata": {},
   "source": [
    "## 项目总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec404252",
   "metadata": {},
   "source": [
    "本次项目利用了RFM均值模型和K-means算法，对于RFM模型的计算标准没有绝对的标准，计算方式可以用均值、分位数、\n",
    "或者打分区间来算，分析师要熟悉不同的业务，针对不同的业务找出最适合的计算方式。RFM模型的优势就是将客户\n",
    "精准化划分，可清楚的了解到公司的客户群体，从而清楚公司的主要收入来源，同时可实现精准化营销。劣势：每个维度划分两个，三个维度将客户划分为8个客户类型，这将极大的消耗公司的人力、物力及时间，同时公司也不可能针对8种客户类型做出8种营销策略。K-means算法的优势就是弥补了RFM模型的短板，将用户尽可能的划分为相同的群体，从而节省公司的人力物力，提高工作效率。\n",
    "注意：\n",
    "1.在K-Means算法中的距离公式，若为有序距离度量一般采用欧式距离或曼哈顿距离，若为无序距离度量，一般采用VDM(Value Difference Metric)\n",
    "2.在K-means算法中，K值的确定公式不唯一，常用的就是肘部法则和轮廓系数法，每种方法各有优劣，可结合具体的实际业务进行响应的选择"
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